Mental health disorders in children of non-drug-abusing parents

Abstract

A representative sample of 58 preschoolers (aged 4 and 5) and 78 school-age children (aged 8 and 9) from methamphetamine-producing (MP) and non-producing (NP) homes was drawn from a rural county in Tennessee, for two separate studies. The researchers assessed the psychological functioning of the children using age appropriate Behavior Assessment System for Children (BASC) forms, and compared the scores of children with NP status with population-based data. The results indicate that in this rural sample, the prevalence of internalizing and externalizing disorders in children from NP homes was higher than in population-based norms. Specifically, the preschoolers showed a higher rate of depression, and the school-age children had higher rates of anxiety, depression, and atypical behaviors than their population-based peers. The results are interpreted in terms of low SES and accessibility to mental health services in rural communities. The authors suggest nurse practitioners include brief psychological screenings in their assessment protocols for this population.

Keywords

child and adolescent mental health child health children low SES mental health psychology rural community

Introduction

The methamphetamine (meth) epidemics that crept into rural Tennessee starting in the late 1990s have had far-reaching consequences on the lives of people who live in the Upper Cumberland region of the state (Asanbe et al., 2008a). Some of the victims with significant negative impact are children who lived with meth-abusing parents in meth-producing (MP) homes (Asanbe et al., 2008a). Research has focused on children with prenatal exposure to meth (Smith et al., 2003; Wouldes et al., 2004) and those who lived in MP homes (Asanbe et al., 2008b; Haight et al., 2005; Ostler et al., 2007). This is understandable. However, an expanded focus on all groups of children who live in these meth-plagued communities, including those from relatively stable non-meth-producing (NP) homes, is in order. We need to have an accurate picture of the prevalence of child psychological problems in rural communities plagued by drugs.

It is important to emphasize that the focus of this paper is on the children from NP homes. However, it is relevant to the understanding of the paper to provide pertinent information on meth and the MP home. According to the US National Drug Intelligence Center (2007), meth is a homemade toxic concoction of household products, farming products, and the active ingredients in some cold medicines. Meth is a stimulant that can be considered as the homemade relative of pharmaceutical-based amphetamine. It is cheap and highly addictive. In the geographical region where this study was conducted, meth is usually made in clandestine labs called meth-lab homes. These clandestine labs can be found in various parts of the home such as kitchen sinks, bathrooms, bedrooms, basements, and car garages. Meth production in these meth-lab homes tends to be made for the users’ own consumption and for their friends, and these activities usually flourish in small rural communities because detection by law enforcement is more difficult. Meth addicts binge on the drug and become irritable and paranoid, and this often leads to acts of violence and child neglect. The ‘tweaking cycle’ when meth users experience feelings of emptiness and sadness is considered the most dangerous because of the potential for unpredictable violent behavior (Sommers and Baskin, 2006).

While many studies indicate that the prevalence of psychological problems are similar among children from rural and urban communities (Gamm et al., 2003; National Advisory Committee on Rural Health, 2002), others have shown that children in rural communities are more likely to be disadvantaged in access to diagnosis and treatment (Amundson, 2001; McCabe and Macnee, 2002). A good understanding of the prevalence of children’s mental health problems and the factors that contribute to the rural–urban disparities can provide a direction for public policy. This paper examines the prevalence of internalizing and externalizing behaviors of children from a rural community in Tennessee with significant meth problems. At the peak of the meth epidemic in 2004, there were 889 meth-lab seizures in Tennessee, and about 75 percent of the seizures occurred in the Upper Cumberland region. The focal community of this study is in this region of the state. The current data are part of a study that looked at the impact of MP home status on the psychological functioning of children from these homes. The findings on the children from MP homes have been previously reported (Asanbe et al., 2008a, 2008b). The goal of this paper is to focus on the children from the NP homes who served as the comparison group, in order to obtain a better picture of their psychological health.

Relative to physical and psychological health, poverty has been a frequent variable in the lives of many rural communities. In many studies, poverty has been associated with more pathology as well as a lower likelihood of seeking and receiving mental health care (Elliot and Larson, 2004; Rost et al., 2002; Stamm, 2003). In addition, limited access to specialty mental health professionals in these communities has also been reported (Gamm et al., 2003; Rost et al., 2002). In fact, recent studies on the directional disparity suggest that up to 95 percent of children living in the smaller areas do not have access to a child psychiatrist (Gamm et al., 2002; Holzer et al., 1998). The combination of poverty and limited access to services could prevent getting a diagnosable child into the mental health system until the behavior deteriorates. Another relevant factor to diagnosis and treatment of mental health problems for adults and children in rural communities is perceived social stigma that limits help-seeking behavior. While mental health stigma is not unique to rural areas, the lack of anonymity in these small communities heightens sensitivity to stigma, and subsequently prevents seeking treatment (Wrigley et al., 2005).

Around the globe, the prevalence of mental health disorders in children is fairly similar, and it ranges from 9.5 percent to 14 percent. According to the literature, about 12 percent of American children and youths have mental health disorders (Costello et al., 2005). More recently, Merikangas et al. (2010) reported that 13 percent of children between the ages of 8 and 15 who participated in the National Health and Nutrition Examination Survey had at least one out of six selected mental health disorders, with 8.6 percent meeting criteria for ADHD, 3.7 percent depression, 2.1 percent conduct disorder, and less than 1 percent for anxiety and eating disorders. Looking at international data, nearly 10 percent of British children aged 5–15 (Ford et al., 2003), 18 percent of Canadian children (Offord et al., 1987), 14 percent of 4–17-year-old Australian children (Sawyer et al., 2001), and 9.4 percent of 8–12 year-olds from a south India district (Hackett et al., 1999) met the criteria for mental health disorders.

Hypothesis

Based on the information presented above, the researchers hypothesized that: (1) children who lived in non-meth-producing (NP) homes would exhibit externalizing behavior problems that are comparable to the community/population-based norms; and (2) children who lived in NP homes would exhibit similar rates of internalizing symptoms as their peers in the community/population-based norms.

For the purpose of this study, an MP home is defined as a residence in which parent meth abusers (father only, mother only, or both parents) produce the drug for consumption and/or sale. An NP home status refers to children who have similar demographic characteristics as children from MP home, but with no known history of living with meth-abusing parents in meth-lab homes. Children from the NP homes serve as the comparison group.

Study site

All the participants for both studies were recruited from a community-based social services program whose goal was to prevent school drop-out in a small rural county in the Upper Cumberland region of Tennessee. A central component of the program was to provide services to children at risk due to poverty and parental meth production and addiction. According to the director of the program, all the participants qualified for school lunch services, an indication of low SES. Participating families with children in preK–6th grades were provided with home visitations, after-school tutoring, and case management services for the household. Referrals to the program were usually from local schools and the county health department. Children recruited for the studies were mostly referred to the community-based program for family related issues such as poverty and a parental history of meth abuse and production. Children living with foster parents were not recruited because of issues relating to parental legal status. The researchers could not obtain reliable information about how long a child resided in the home when meth production was present. The MP participants who had been removed from home less than three months previously were excluded from the study to minimize traumatic intervening events that may be associated with their removal, as this could potentially bias the data. The study protocol and consent forms were approved by the institutional review board (IRB) of Tennessee Technological University.

Method

Participants

In the first study, the researchers assessed the behavioral and emotional functioning of 78 Caucasian school-age children (mean age 8.4); 40 from MP homes and 38 from NP homes. The children individually rated themselves using the Behavior Assessment System for Children (BASC-SRP; Reynolds and Kamphaus, 1992). For the second study, the researchers assessed the externalizing and internalizing behaviors of 58 Caucasian preschoolers (mean age 4.5). Thirty-one preschoolers from MP homes and 27 from NP homes were assessed using the BASC-PRS-P (Reynolds and Kamphaus, 1998). Twenty-five custodial grandmothers and six aunts (MP group) and 27 biological mothers (NP group) rated the children during the regular home visitation sessions.

Procedure

Based upon a review of each child’s chart by the director of the program, children were assigned to one of two groups: MP home and NP home. The inclusion criteria for the MP group was as follows: (1) the preschool child should be between the ages of 4 and 5 and the school-age child should be between the ages of 8 and 9; (2) the child has a history of being removed from a meth-lab home; (3) the removal from the home took place at least three months prior to the study; (4) the child was currently living with a family member and not with a foster parent; (5) the child did not have known major medical illness; and (6) the child did not have a history of prenatal exposure to meth. For the NP group, the child must meet all the inclusion criteria as well not having a known history of living with meth-abusing parents in a meth-lab home. For both studies, a licensed professional counselor administered the instruments from the summer of 2004 through the summer of 2005.

An important distinction between the two groups is that all the children in the MP group had been removed from their parents’ home and were currently placed with relatives (grandparents, aunts and uncles) by child protective services. On the other hand, all participants in the NP group lived with their parents. Thus, a history of living in a meth-lab home is confounded with children being removed from the home. The demographic descriptors of children and their parents are presented in Tables 1 and 2.

Table 1.

Demographic descriptors of school-age children by group (meth-producing vs non-producing)

Demographic variables	MP	NP
Participants	40 (51.3%)	38 (48.7%)
Children’s mean age	8.4	8.45
8 years old	24 (60%)	21 (55.3%)
9 years old	16 (40%)	17 (44.7%)
Female	20 (50%)	18 (47.4%)
Male	20 (50%)	20 (52.6%)
Second/Third Grades	26 (65%)	31 (81.6%)
Fourth Grade	14 (35%)	7 (18.4%)

Table 2.

Demographic descriptors of preschoolers and raters by group (meth-producing vs non-producing)

Demographic variables	MP	NP
Participants	31 (53.4%)	27 (46.6%)
Children’s mean age	4.53	4.59
4 years old	14 (46.6%)	16 (53.4%)
5 years old	16 (57%)	12 (43%)
Female	12 (38.7%)	15 (55.6%)
Male	19 (61.3%)	12 (44.4%)
Rater – biological mother	0 (0%)	27 (100%)
Rater – grandmother	25 (80.64%)	0 (0%)
Rater – aunt	6 (19.36%)	0 (0%)

Measures

Two age-appropriate instruments (BASC-PRS-P and BASC-SRP) were used to assess the preschoolers and school-age children respectively. The BASC-PRS-P (M = 50, SD = 10), is a 131-item questionnaire that asks parents to assess a broad range of behavioral and emotional adjustment of children between the ages of 2½ and 5 years. Each parent rates the behavior of the focal child on a 4-point response format. The test produces four broad domain (composite) scores: Externalizing Problems (EP), Internalizing Problems (IP), Behavioral Symptoms Index (BSI) and Adaptive Skills (AS) that are computed from eight relevant clinical and two adaptive scales. The BASC-SRP (M = 50, SD = 10), is a self-report instrument that is commonly used to assess children between the ages of 8 and 11. The test consists of 152 items that ask children to indicate whether each statement on the test is true or false. The BASC-SRP produces four broad domain (composite) scores: School Maladjustment (SM), Clinical Maladjustment (CM), Personal Adjustment (PA) and Emotional Symptoms Index (EMI). The domain scores are computed from eight relevant clinical and four adaptive scales. For both measures, T scores that are greater than 70 fall in the clinical range and signify problems that require attention, while T scores of 60 or more fall in the at-risk range. For adaptive behaviors, T scores that are 40 or less fall in the at-risk range. Both measures show adequate reliability and the scales correlate fairly highly with corresponding scales on the Child Behavior Checklist (CBCL; Achenbach, 1991) and the Achenbach’s Youth Self-Report (AYS; Achenbach and Edelbrock, 1987).

Results

For the school-age study, three chi-squares were conducted to assess if Age, Gender or Grade differed by MP Group (MP vs NP). Results of the chi-square suggest that no significant differences exist on Age between MP and NP participants, χ² (1) = 0.18, p = .67 and on Gender by Group, χ² (1) = 0.05, p = .82. For Grade by MP Group, two cells had expected counts less than five which violates test assumptions. The Second and Third Grade levels were collapsed into one category and the analysis was conducted on Grade (Second/Third vs Fourth) by MP Group. All assumptions were met. Results of the chi-square suggest that there is no significant difference between MP and NP participants on Grade level, χ² (1) = 2.72, p = .10. Further analyses were completed to compare the number of children in each group whose ratings exceeded the clinical cutoff (i.e. at score < 70). The following data show the number of participants with scores in the clinical range in the four composite scales: 13 (32.5%) MP and 2 (5.2%) NP participants in School Maladjustment, 6 (15%) MP and 3 (7.9%) NP participants in Clinical Maladjustment, 11 (27.5%) MP and 4 (10.5%) NP participants in Personal Adjustment, and 9 (22%) MP and 7 (18.4%) NP participants in Behavior Symptoms Index scale. Similar comparisons were made on the 12 individual subscales.

A MANOVA was further conducted on the school-age data and the results suggest there is a significant difference between MP and NP participants on School Maladjustment (Attitudes toward Teachers and Attitudes toward School) scores, F (2, 75) = 4.19, p < .05 (η² = .10, Power = .72). Univariate ANOVAs revealed a significant difference in Attitudes toward Teacher by MP Group, F (1, 76) = 8.22, p < .01 (η² = .10, Power = .81). MP participants (M = 60.05, SD = 14.48) scored significantly higher on Attitudes toward Teacher compared to the NP participants (M = 52.11, SD = 9.28). Further analysis suggests that there is no significant difference between the MP and NP groups on Emotional Symptom Index, F (1, 76) = 1.44, p = 0.23, ns (η² = .04, Power = .31).

With specific reference to the individual scales for the NP group, Table 3 shows that 18.4 percent of the children evidenced clinical level scores in Anxiety, 15.7 percent in Depression, 15.7 percent in Atypical behaviors, and 13 percent in negative Attitude toward School. These figures indicate concern in both Internalizing and Externalizing behaviors. On a positive note, close to a quarter (23.6%) of these children report high self-esteem scores.

Table 3.

BASC-SRP subscale cases exceeding clinical level cutoff by group (meth-producing vs non-producing)

Subscale	MP clinical n (%)	NP clinical n (%)
Attitude to school	12 (30%)	5 (13%)
Attitude to teachers	14 (35%)	4 (10.5%)
Atypicality	12 (30%)	6 (15.7%)
Locus of control	6 (15%)	2 (5.2%)
Social stress	9 (22.5%)	4 (10.5%)
Anxiety	9 (22.5%)	7 (18.4%)
Depression (inverse)	12 (30%)	6 (15.7%)
Sense of inadequacy	12 (30%)	4 (10.5%)
Relations with parents	8 (20%)	4 (10.5%)
Interpersonal relationships (inverse)	11 (27.5%)	4 (10.5%)
Self-esteem	10 (25%)	9 (23.6%)
Self-reliance	4 (10%)	3 (7.9%)

Notes: T-scores > 70 on maladaptive behaviors fall in the clinical elevated range. T-scores > 60 on maladaptive behaviors fall in the at-risk range. T-scores < 40 on adaptive behaviors fall in the at-risk range.

For the preschool data, two chi-squares were conducted to assess if Age or Gender differed by MP Group (Meth-Producing vs Non-Producing). Results of the chi-square suggest that no significant differences exist on Age by Group, χ² (1) = 1.15, p = .28 and on Gender by Group, χ² (1) = 1.65, p = .20. As with the school-age data, further analyses were completed to compare the number of children in each group with caregiver/parent ratings, that exceeded the clinical cutoff (i.e. at score < 70). The following data show the number of participants who had scores in the clinical range in the four composite scales: 13 (42%) MP and 4 (14.8%) NP participants in Externalizing Behaviors, 9 (30%) MP and 6 (22%) NP participants in Internalizing Behaviors, 9 (30%) MP and 3 (11%) NP participants in Adaptive Skills, and 11 (35.5%) MP and 2 (7.4%) NP participants in Behavior Symptoms Index scale. Similar comparisons were made on the 10 individual subscales.

A MANOVA conducted on the Externalizing Problems (Hyperactivity and Aggression) revealed a significant difference on Externalizing Problems by MP Group, F (2, 55) = 3.44, p < .05 (η² = .11, Power = .62). Univariate ANOVAs revealed a significant difference on Aggression by MP Group, F (1, 56) = 6.86, p < .01 (η² = .11, Power = .73). MP participants (M = 65.52, SD = 16.03) scored significantly higher on Aggression compared to NP participants (M = 55.37, SD = 13.04). For Internalizing Problems, results of a MANOVA suggest that there is no significant difference by MP Group, F (3, 54) = 0.97, ns (η² = .05, Power = .25).

When we look at the individual scales again for preschoolers with NP status, Table 4 shows that 22 percent of the children had scores in the clinical range in Depression, 14.8 percent in Atypical Behaviors, and 11 percent in Hyperactivity. It should be noted that a lower percentage (11%) reported age-appropriated scores in social skills compared to 19 percent of children from the MP homes.

Table 4.

BASC-PRS-P subscale cases exceeding clinical level cutoff by group (meth-producing vs non-producing)

Subscale	MP clinical cases %	NP clinical cases %
Hyperactivity	7 (22.5%)	3 (11%)
Aggression	13 (42%)	2 (7.4%)
Anxiety	5 (16%)	2 (7.4%)
Depression	12 (38.7%)	6 (22%)
Somatization	6 (19.3%)	3 (11%)
Atypicality	10 (32.2%)	4 (14.8%)
Withdrawal	3 (9.67%)	2 (7.4%)
Attention problems	4 (12.9%)	1 (3.7%)
Adaptability	5 (16%)	1 (3.7%)
Social skills	6 (19%)	3 (11%)

Discussion

The focus of this paper is on the children from NP homes because, as already mentioned, the data for children in the MP group have been discussed in details elsewhere (Asanbe et al., 2008a, 2008b). For this sample, a notable percentage of children from NP homes had clinical level scores in internalizing and externalizing behaviors. This finding is contrary to the two predictions for this study. Starting with the school-age data, many NP participants have clinical level scores that exceeded the rate reported in population and community-based studies. For example, Merikangas and research colleagues (2010) reported a prevalence rate of 3.7 percent for depression, 2.1 percent for conduct disorder, and less than 1 percent for anxiety disorder. For our sample, 15.7 percent of the NP participants had clinical level scores in depression, 15.7 percent in atypical behaviors, and 18.4 percent in anxiety problems. The picture for the preschoolers is more troubling as their parents’ ratings suggest that depression may be a concern for 22 percent of these children. These findings raise a critical question – why is the prevalence rate of both internalizing and externalizing behavior problems high for children from relatively stable, non-drug-abusing parents? The answer(s) could inform public policy, especially, in prevention and delivery of effective interventions for children from rural communities plagued by drugs.

For a start, it is important to look at these findings in relation to what happened in this community during the last decade. As mentioned earlier, at the time the data were collected, this region was devastated by the meth epidemic, and this could have impacted the findings, even for children in the NP group. With the meth epidemic in the background, poverty may offer a plausible explanation for the findings, since it is associated with increased psychopathology. A closer look at the demographics of the focal community in the two studies shows that approximately, 15.2 percent of the county residents live below the poverty level. The 2001–2002 school year free and reduced lunch rate for the county’s schools was 50.1 percent, greater than the state average of 42.9 percent. With respect to education, about 64% of the children live in homes in which the primary wage earner has less than a high school education (US Census Bureau, 2006). The lack of education fuels the cycle of poverty and poor economic conditions. With specific reference to the sample for this study, all the participants qualified for school lunch services, an indication of low SES. As the literature review indicates, poverty has been associated with increased pathology and a lower likelihood of seeking and receiving mental health care (Elliot and Larson, 2004; Fox et al., 2001; Rost et al., 2002; Stamm, 2003). And, with limited accessibility to services, children who may have diagnosable problems may not get into the mental health system until the behavior manifests into more serious problems.

On a positive note, recent developments that could improve the picture of mental health in rural children, especially, for children in the meth-plagued communities are beginning to emerge. The new mental health parity law, which is expected to improve reimbursement for mental health services in the United States, may improve the recruitment of specialty mental health professionals to the rural areas. Incentives, such as the physician loan forgiveness program, are being extended in few communities. This may result in more child psychologists working in rural counties. In conjunction with the measures above, there are other steps that can help to narrow the rural disparity in mental health diagnosis and treatment for children. As mentioned earlier, stigma limits help-seeking behavior, and general outreach education to increase anonymity and acceptance can help to combat this (Rost et al., 2002). In addition, incorporating mental health services for children into a primary care can be a step in the right direction.

In terms of drug policies, national, state, and local efforts have had some impact on curbing the scourge of meth in rural areas. A case in point: since the Meth-Free Tennessee Act that restricted the sale of meth precursors (pseudoephedrine products) became law in 2005, the rate of meth-lab seizures has been reduced from about 746 in 2005 to about 334 in 2006 (Tennessee Bureau of Investigation, 2006). At the national level, the US Congress enacted the Combat Meth Epidemic Act of 2005 to fight the production and distribution of meth. All these initiatives may be having a positive impact because according to the National Clandestine Laboratory Seizure System data (NDIC, 2007), the number of reported meth-lab seizures decreased nationwide by about 43 percent from 10,212 in 2003 to 5846 in 2005. We (the authors) surmise that the declining meth problems will positively impact the psychological health of all groups of children in these rural communities devastated by meth. In the area of research, the literature on meth environment and child psychological welfare is improving. The 2005 Methamphetamine Remediation Research Act (109th Congress) provides resources to study the long-term health impacts of meth exposure on children removed from meth-lab homes. This is a good start, but more needs to be done.

There are limitations to this study starting with limited sample size. The data are also based on the reports of parents and self-reports of children who received community-based social services in a single geographic area of Tennessee. Although the participants’ characteristics are comparable to other rural counties in this region of the state, characteristics may vary across other regions of the country.

In conclusion, although children from the NP group in both studies did not live in a meth home environment, the data show that many of them have elevated scores in a number of psychological areas of functioning that warrant our attention. The nature of this study makes it difficult to draw a direct connection between living in an NP home and subsequent mental health issues. However, for this population, we can infer a connection based on other variables such as their SES, the geographical location of the participants, accessibility to mental health care, and the comparison with national and international norms. Based on the data, it is not an exaggeration to say that children who grow up in non-drug-abusing homes in rural communities are also vulnerable to behavioral and social emotional problems. While the vulnerability is not alarming when compared to their peers with drug-abusing parents, we should be vigilant to identify children whose vulnerability may be due, in part, to low SES.

The findings from this study have important implications for Psychiatric Mental Health Nurse Practitioners. A better understanding of the psychological functioning of these children could help these professionals and other child welfare agencies in developing new levels of service. Adding brief psychological screenings to assessment protocols can help these professionals to provide a more comprehensive treatment for this population.

Footnotes

Acknowledgments

This study was supported by the Tennessee Technological University Faculty Grant. The authors wish to express their gratitude to the parents and children who participated in the study.

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